Time of use Period Determination for Residential Customers in Peninsular Malaysia
N. Khamis1, C.S. Tan2
1N. Khamis, College of Graduate Studies,Universiti Tenaga Nasional Jalan IKRAM-UNITEN, 43000 Kajang Selangor, Malaysia.
2C.S. Tan, Institute of Energy Policy and Research, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000, Selangor, Malaysia.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4246-4249 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4938129219/2019©BEIESP | DOI: 10.35940/ijeat.B4938.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Time of Use (TOU) is basically one of the demand response programs which enable the end-user consumers to adjust their energy use in response to changes in electricity prices over a period of time with an incentives. Generally, Time-of-Use implementation helpto reduce system’s maximum demand by transferring some of the demand into different hours. Time-ofUse also is a cost reflective electricity pricing scheme in which days are commonly split into multiple periods such as peak, midpeak and off-peak. The residential sector is expected to have the highest growth as compared to commercial and industrial sectors. This is due to an increase in population and increasing living standards which increase the number of households and the electrical electricity consumptionper householdas more households and individuals choose to buy more electrical appliances. This paper presented a new clustering method called Jenks Natural Breaks in order to segmentize the Time of Use period for the residential customers in Peninsular Malaysia. A comparison of K-Means clustering method and the proposed Jenks Natural Breaks method is presented in this paper. The time of use determination are performed using these two methods based on the average of six actual residential customer’s load profiles. In this paper, two-part periods (zones) segmentation of TOU areconsidered for analysis and discussions. The results shows the TOU Peak period using the K-Means clustering method is between 10.00am and 8.00pm while for a new proposed Jenks Natural Breaks method the TOU Peak period is between 9.00am and 8.00pm.
Keywords: Time of Use, Residential, Jenks Natural Breaks, k-Means clustering.